Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting

• URL: http://arxiv.org/abs/2501.16591v1
• Date: Tue, 28 Jan 2025 00:12:26 GMT
• Title: Applying Ensemble Models based on Graph Neural Network and Reinforcement Learning for Wind Power Forecasting
• Authors: Hongjin Song, Qianrun Chen, Tianqi Jiang, Yongfeng Li, Xusheng Li, Wenjun Xi, Songtao Huang,
• Abstract summary: We propose an ensemble model based on graph neural networks and reinforcement learning (EMGRL) for Wind Power Forecasting (WPF) Our approach includes: (1) applying graph neural networks to capture the time-series data from neighboring wind farms relevant to the target wind farm; (2) establishing a general state embedding that integrates the target wind farm's data with the historical performance of base models on the target wind farm; and (3) ensembling and leveraging the advantages of all base models through an actor-critic reinforcement learning framework for WPF.
• Score: 1.4710752403175422
• License:
• Abstract: Accurately predicting the wind power output of a wind farm across various time scales utilizing Wind Power Forecasting (WPF) is a critical issue in wind power trading and utilization. The WPF problem remains unresolved due to numerous influencing variables, such as wind speed, temperature, latitude, and longitude. Furthermore, achieving high prediction accuracy is crucial for maintaining electric grid stability and ensuring supply security. In this paper, we model all wind turbines within a wind farm as graph nodes in a graph built by their geographical locations. Accordingly, we propose an ensemble model based on graph neural networks and reinforcement learning (EMGRL) for WPF. Our approach includes: (1) applying graph neural networks to capture the time-series data from neighboring wind farms relevant to the target wind farm; (2) establishing a general state embedding that integrates the target wind farm's data with the historical performance of base models on the target wind farm; (3) ensembling and leveraging the advantages of all base models through an actor-critic reinforcement learning framework for WPF.

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